chapter3

Chapter 3
Foundations of Business
Intelligence: Databases and
Information Management
THE DATA HIERARCHY
TRADITIONAL FILE PROCESSING
Organizing Data in a Traditional File Environment
• Problems with the traditional file environment
– Data redundancy:
• Presence of duplicate data in multiple files
– Data inconsistency:
• Same attribute has different values
– Program-data dependence:
• When changes in program requires changes to data accessed by
program
– Lack of flexibility
– Poor security
– Lack of data sharing and availability
The Database Approach to Data Management
• Database
– Serves many applications by centralizing data and controlling redundant data
• Database management system (DBMS)
– Interfaces between applications and physical data files
– Separates logical and physical views of data
– Solves problems of traditional file environment
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Controls redundancy
Eliminates inconsistency
Uncouples programs and data
Enables organization to central manage data and data security
HUMAN RESOURCES DATABASE WITH MULTIPLE VIEWS
A single human resources database provides many different views of data, depending on the information
requirements of the user. Illustrated here are two possible views, one of interest to a benefits specialist and
one of interest to a member of the company’s payroll department.
Relational DBMS
– Represent data as two-dimensional tables
– Each table contains data on entity and attributes
– Rows (tuples): Records for different entities
– Fields (columns): Represents attribute for entity
– Key field: Field used to uniquely identify each record
– Primary key: Field in table used for key fields
– Foreign key: Primary key used in second table as look-up field to
identify records from original table
Relational Database Tables
Illustrated here are
tables for the
entities SUPPLIER
and PART showing
how they represent
each entity and its
attributes. Supplier
Number is a primary
key for the SUPPLIER
table and a foreign
key for the PART
table.
THE THREE BASIC OPERATIONS OF A RELATIONAL DBMS
The Database Approach to Data Management
• Capabilities of database management systems
– Data definition capability: Specifies structure of database content,
used to create tables and define characteristics of fields
– Data dictionary: Automated or manual file storing definitions of data
elements and their characteristics
– Data manipulation language: Used to add, change, delete, retrieve
data from database
• Structured Query Language (SQL)
• Microsoft Access user tools for generating SQL
– Many DBMS have report generation capabilities for creating polished
reports (Crystal Reports)
MICROSOFT ACCESS DATA DICTIONARY FEATURES
Microsoft Access has a rudimentary data dictionary capability that displays information about the size, format, and other
characteristics of each field in a database. Displayed here is the information maintained in the SUPPLIER table. The small key
icon to the left of Supplier_Number indicates that it is a key field.
AN ACCESS QUERY
FIGURE 6-8
Illustrated here is how the query in Figure 6-7 would be constructed using Microsoft Access query building
tools. It shows the tables, fields, and selection criteria used for the query.
The Database Approach to Data Management
• Designing Databases
– Conceptual (logical) design: abstract model from business perspective
– Physical design: How database is arranged on direct-access storage devices
• Design process identifies:
– Relationships among data elements, redundant database elements
– Most efficient way to group data elements to meet business requirements, needs of
application programs
• Normalization
– Streamlining complex groupings of data to minimize redundant data elements and
awkward many-to-many relationships
AN UNNORMALIZED RELATION FOR ORDER
NORMALIZED TABLES CREATED FROM ORDER
After normalization, the original relation ORDER has been broken down into four smaller relations. The
relation ORDER is left with only two attributes and the relation LINE_ITEM has a combined, or concatenated,
key consisting of Order_Number and Part_Number.
The Database Approach to Data Management
• Referential integrity rules
• Used by RDMS to ensure relationships between tables remain consistent
• Entity-relationship diagram
– Used by database designers to document the data model
– Illustrates relationships between entities
– Caution: If a business doesn’t get data model right, system
won’t be able to serve business well
AN ENTITY-RELATIONSHIP DIAGRAM
Big Data
• Massive sets of unstructured/semi-structured data from
Web traffic, social media, sensors, and so on
• Petabytes, Exabytes of data: Volumes too great for typical
DBMS
• Can reveal more patterns and anomalies
Using Databases to Improve Business Performance and
Decision Making
• Business intelligence infrastructure
– Today includes an array of tools for separate systems, and big data
• Contemporary tools:
– Data warehouses
– Data marts
– Hadoop
– In-memory computing
– Analytical platforms
Using Databases to Improve Business Performance and
Decision Making
• Data warehouse:
– Stores current and historical data from many core operational transaction
systems
– Consolidates and standardizes information for use across enterprise, but data
cannot be altered
– Provides analysis and reporting tools
• Data marts:
– Subset of data warehouse
– Summarized or focused portion of data for use by specific population of users
– Typically focuses on single subject or line of business
COMPONENTS OF A DATA WAREHOUSE
A contemporary business
intelligence infrastructure
features capabilities and
tools to manage and
analyze large quantities and
different types of data from
multiple sources. Easy-touse query and
reporting tools for casual
business users and more
sophisticated analytical
toolsets for power users
are included.
Using Databases to Improve Business Performance and
Decision Making
• Hadoop
– Enables distributed parallel processing of big data across
inexpensive computers
– Key services
• Hadoop Distributed File System (HDFS): data storage
• MapReduce: breaks data into clusters for work
• Hbase: NoSQL database
– Used by Facebook, Yahoo, NextBio
Using Databases to Improve Business Performance and
Decision Making
• In-memory computing
– Used in big data analysis
– Use computers main memory (RAM) for data storage to avoid delays
in retrieving data from disk storage
– Can reduce hours/days of processing to seconds
• Analytic platforms
– High-speed platforms using both relational and non-relational tools
optimized for large datasets
Analytical tools: Relationships, patterns, trends
– Tools for consolidating, analyzing, and providing access to
vast amounts of data to help users make better business
decisions
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Multidimensional data analysis (OLAP)
Data mining
Text mining
Web mining
Online analytical processing (OLAP)
– Supports multidimensional data
analysis
• Viewing data using multiple
dimensions
• Each aspect of information
(product, pricing, cost, region,
time period) is different
dimension
• Example: How many washers
sold in East in June compared
with other regions?
– OLAP enables rapid, online
answers to ad hoc queries
Data mining
– Finds hidden patterns, relationships in datasets
• Example: customer buying patterns
– Infers rules to predict future behavior
– Types of information obtainable from data mining:
• Associations: Occurrences linked to single event
• Sequences: Events linked over time
• Classification: Recognizes patterns that describe group to which item
belongs
• Clustering: Similar to classification when no groups have been defined;
finds groupings within data
• Forecasting: Uses series of existing values to forecast what other
values will be
Text mining
– Extracts key elements from large unstructured data sets
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Stored e-mails
Call center transcripts
Legal cases
Patent descriptions
Service reports, and so on
– Sentiment analysis software
• Mines e-mails, blogs, social media to detect opinions
Web mining
– Discovery and analysis of useful patterns and
information from Web
– Understand customer behavior
– Evaluate effectiveness of Web site, and so on
– Web content mining
• Mines content of Web pages
– Web structure mining
• Analyzes links to and from Web page
– Web usage mining
• Mines user interaction data recorded by Web server
LINKING INTERNAL DATABASES TO THE WEB
Managing Data Resources
• Data quality audit:
– Structured survey of the accuracy and level of completeness of the
data in an information system
• Survey samples from data files, or
• Survey end users for perceptions of quality
• Data cleansing
– Software to detect and correct data that are incorrect, incomplete,
improperly formatted, or redundant
– Enforces consistency among different sets of data from separate
information systems